It is very easy to plot chart in plotly express!:)
Let's import the required libraries first:
# !pip3 install plotly
%%HTML
<script src="require.js"></script>
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=False)
# import plotly.io as pio
# pio.renderers.default = "notebook"
# import plotly.offline as pyo
# Set notebook mode to work in offline https://www.pythonfixing.com/2021/11/fixed-plotly-chart-not-showing-in.html
# pyo.init_notebook_mode()
df = pd.DataFrame({"Date": ["2023-01-01", "2023-01-02", "2023-01-03", "2023-01-04", "2023-01-05", "2023-01-06", "2023-01-07",
"2023-01-08", "2023-01-09", "2023-01-10"],
"Analysis": ["Normal", "Anomaly", "Normal", "Anomaly", "Anomaly", "Normal", "Normal", "Normal", "Normal", "Normal"],
"Sales": [120, 30, 115, 10, 5, 100, 99, 123, 134, 96]})
df_with_group = pd.DataFrame({
"Date": ["2023-01-01", "2023-01-02", "2023-01-03", "2023-01-04", "2023-01-05", "2023-01-06", "2023-01-07",
"2023-01-08", "2023-01-09", "2023-01-10", "2023-01-01", "2023-01-02", "2023-01-03", "2023-01-04",
"2023-01-05", "2023-01-06", "2023-01-07", "2023-01-08", "2023-01-09", "2023-01-10"],
"Analysis": ["Normal", "Anomaly", "Normal", "Anomaly", "Anomaly", "Normal", "Normal", "Normal", "Normal", "Normal",
"Anomaly", "Normal", "Normal", "Anomaly", "Anomaly", "Normal", "Normal", "Normal", "Normal", "Anomaly"],
"Sales": [120, 30, 115, 10, 5, 100, 99, 123, 134, 96,
15, 100, 103, 13, 8, 88, 111, 126, 120, 25],
"Sales_Group": ["A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B"]
})
df.Date = pd.to_datetime(df.Date)
df_with_group.Date = pd.to_datetime(df_with_group.Date)
df.head()
| Date | Analysis | Sales | |
|---|---|---|---|
| 0 | 2023-01-01 | Normal | 120 |
| 1 | 2023-01-02 | Anomaly | 30 |
| 2 | 2023-01-03 | Normal | 115 |
| 3 | 2023-01-04 | Anomaly | 10 |
| 4 | 2023-01-05 | Anomaly | 5 |
df_with_group.tail()
| Date | Analysis | Sales | Sales_Group | |
|---|---|---|---|---|
| 15 | 2023-01-06 | Normal | 88 | B |
| 16 | 2023-01-07 | Normal | 111 | B |
| 17 | 2023-01-08 | Normal | 126 | B |
| 18 | 2023-01-09 | Normal | 120 | B |
| 19 | 2023-01-10 | Anomaly | 25 | B |
Here is a simple way to plot a animation chart!
fig = px.line(df, x="Date", y="Sales", title='Daily Sales')
fig.show(renderer='notebook')
And if we want to see 2 sales group's performance together:
fig2 = px.line(df_with_group, x="Date", y="Sales", color='Sales_Group', title='Daily Sales')
fig2.show()
Now it is time to markthe anomalies! We will use Graph objects for this. Start with mark the anomaly dates:
anomaly_date = df[df.Analysis == "Anomaly"]
anomaly_date
| Date | Analysis | Sales | |
|---|---|---|---|
| 1 | 2023-01-02 | Anomaly | 30 |
| 3 | 2023-01-04 | Anomaly | 10 |
| 4 | 2023-01-05 | Anomaly | 5 |
fig = px.line(df, x="Date", y="Sales", title='Daily Sales')
fig.add_traces(go.Scatter(x=anomaly_date["Date"], y=anomaly_date["Sales"], mode="markers", name="Anomaly",
hoverinfo="skip"))
fig.show(renderer='notebook')
If you want to see the "Anomaly" at the chart, you should use "add_annotation":
fig = px.line(df, x="Date", y="Sales", title='Daily Sales')
fig.add_traces(go.Scatter(x=anomaly_date["Date"], y=anomaly_date["Sales"], mode="markers", name="Anomaly",
hoverinfo="skip"))
for idx in range(len(anomaly_date)):
fig.add_annotation(dict(font=dict(color='rgba(0,0,200,0.8)',size=12),
x=anomaly_date["Date"].iloc[idx],
y=anomaly_date["Sales"].iloc[idx],
showarrow=False,
text=anomaly_date["Analysis"].iloc[idx],
textangle=0,
xanchor='auto', #['auto', 'left', 'center', 'right']
xref="x",
yref="y"))
fig.show(renderer='notebook')
And if we want to see 2 sales group's performance together:
# filter anomaly dates
anomaly_date_2 = df_with_group[df_with_group.Analysis == "Anomaly"]
# line plot
fig2 = px.line(df_with_group, x="Date", y="Sales", color='Sales_Group', title='Daily Sales')
# add marker
fig2.add_traces(go.Scatter(x=anomaly_date_2["Date"], y=anomaly_date_2["Sales"], mode="markers", name="Anomaly",
hoverinfo="skip"))
# add annotation
for idx in range(len(anomaly_date_2)):
fig2.add_annotation(dict(font=dict(color='rgba(0,0,200,0.8)',size=12),
x=anomaly_date_2["Date"].iloc[idx],
y=anomaly_date_2["Sales"].iloc[idx],
showarrow=False,
text=anomaly_date_2["Analysis"].iloc[idx],
textangle=0,
xanchor='auto', #['auto', 'left', 'center', 'right']
xref="x",
yref="y"))
fig2.show(renderer='notebook')
!jupyter nbconvert --to html Line_Chart_with_Plotly.ipynb
front_matter_str = """--- layout: post title: 2023-01-10-Line_Chart_with_Plotly subtitle: I would like to explain the plotly express line chart and how to add marks to the chart. tags: [plotly,visulatisation] ---"""
import subprocess
def conv_nb_jekyll(filename,front_matter):
"""
this function will convert your jupyter notebook to html and
prepend the front matter string you provide to the top of the resulting html file
Args:
filename: filename of input jupyter notebook (.ipynb file)
front_matter: python formatted string resembling YAML jekyll front matter
Returns:
jekyll_html_post: location of final html file to post on your jekyll blog
"""
#convert jupyter notebook to html
subprocess.call(["jupyter", "nbconvert","--to","html","--template","full",filename])
#function that will prepend given string to given filename
def prepend_string(filename, string):
with open(filename, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write(string.rstrip('\r\n') + '\n' + content)
#call function to prepend front matter to the file
html_file = filename.replace('.ipynb','.html')
prepend_string(html_file,front_matter)
print('converted html file at: ',html_file)
return html_file
jekyll_html_post = conv_nb_jekyll(filename='2023-01-10-Line_Chart_with_Plotly.ipynb',front_matter=front_matter_str)